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A comparison of local variance, fractal dimension, and Moran's index as aids to multispectral image classification / C.W. Emerson in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 8 (April 2005) . - pp 1575 - 1588 Titre : A comparison of local variance, fractal dimension, and Moran's index as aids to multispectral image classification Type de document : Article/Communication Auteurs : C.W. Emerson, Auteur ; N. Siu-Ngan Lam, Auteur ; D.A. Quattrochi, Auteur Année de publication : 2005 Article en page(s) : pp 1575 - 1588 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] Atlanta (Géorgie) [Termes IGN] autocorrélation spatiale [Termes IGN] classification par maximum de vraisemblance [Termes IGN] ERDAS Imagine [Termes IGN] image Landsat-ETM+ [Termes IGN] image multibande [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] segmentation d'image [Termes IGN] texture d'image Résumé : (Auteur) The accuracy of traditional multispectral maximum-likelihood image classification is limited by the multi-modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery with the goal of improving urban land cover classification accuracy. Tools available in the ERDAS Imagine™ software package and the Image Characterization and Modeling System (ICAMS) were used to analyse Landsat ETM+ imagery of Atlanta, Georgia. Images were created from the ETM+ panchromatic band using the three texture indices. These texture images were added to the stack of multispectral bands and classified using a supervised, maximum likelihood technique. Although each texture band improved the classification accuracy over a multispectral only effort, the addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques. Numéro de notice : A2005-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331326765 En ligne : https://doi.org/10.1080/01431160512331326765 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27341 [article]

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